Abstract
The traditional Hough transform needs the edge detection in advance, so the effect of edge detection influences the final fitting result. This paper proposes a new method of detecting parabolas using the kernel density estimate based on the theory of Rozenn Dahyot, and extends this method into the eyelid detection in noisy images and other images including parabolas. In our paper, the edge detection is not necessary. On one hand, we not only consider the current points on the parabola, but also ones around the parabola. Experiments demonstrate that the proposed algorithm is robust and insensitive to the noise.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Daugman, J.: How Iris Recognition Works. IEEE Transactions on Circuit and System for Video Technology 14(1), 21–30 (2004)
Wildes, R.: Iris Recognition: an Emerging Biometric Technologh. Proceeding of the IEEE 85(9), 1348–1363 (1997)
Pardas, M.: Extraction and Tracking of the Eyelids. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Istanbul, pp. 2357–2360 (2000)
Rozenn, D.: Statistical Hough Transform. IEEE Transactions on Pattern Analysis and Machine Intelligence (submission) (2008)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. In: Monographs on Statistics and Applied Probability, London (1986)
Radon Transform, http://mathworld.wolfram.com/RadonTransform.html
Deans, S.R.: Hough Transform from the Radon Transform. IEEE Transactions on Pattern Analysis and Machine Intelligence 3(2) (1981)
Dahyot, R., Wilson, S.: Robust Scale Estimation for the Generalized Gaussian Probability Density Function. Advances in Methodology and Statistics (Metodolo ski zvezki) 3(1), 21–37 (2006)
UBIRIS. v2 Database. Website (2008), http://iris.di.ubi.pt/ubiris2.html
Blake, A., Isard, M.: Active Contours. Springer, London (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, X., Song, Q., Li, P. (2009). A Parabolic Detection Algorithm Based on Kernel Density Estimation. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-04070-2_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04069-6
Online ISBN: 978-3-642-04070-2
eBook Packages: Computer ScienceComputer Science (R0)